MPP: A Join-dividing Method for Multi-table Privacy Preservation

摘要

In regard to relational databases, studies in this area typically focus on individual privacy leakage in one table. However, in reality, a database usually has many tables, some of them contain correlation information about individual, which can provide additional implication as background knowledge to attacker. In this paper, we innovatively propose a new method named MPP (Multi-table Privacy Preservation) which combines Lossy-join with Bucketization to enhance the individual privacy in database. We consider the privacy disclosure problem from the global sight of the entire dataset instead of a table. Based on this method, we not only solve the correlation information leakage by other tables, but also improve the data utility. Extensive experiments on 32.8GB real-world Express data demonstrate the effectiveness and efficiency of our approach in terms of data utility and computational cost.